Made available in DSpace on 2014-06-11T19:26:55Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-02-26Bitstream added on 2014-06-13T19:47:27Z : No. of bitstreams: 1 almeida_mp_me_sjrp.pdf: 4937582 bytes, checksum: 4d9c67bc8dda1a2e1a742ee59be238fa (MD5) / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Neste trabalho propõe-se um filtro mediano seletivo e um filtro híbrido para eliminação de ruído impulsivo em imagens digitais monocromáticas. O primeiro é baseado em uma modificação do filtro mediano por meio de um detector de ruído impulsivo. O segundo é obtido combinando-se o filtro mediano seletivo com um modelo de retoque digital. A remoção de ruído impulsivo em uma imagem colorida é realizada por meio da extensão dos filtros propostos para cada canal de cor da imagem. Os experimentos realizados indicam que os métodos propostos são eficazes na restauração de imagens com grandes densidades de ruído. / In this paper a selective median filter and a hybrid filter for removing impulsive noise in digital grayscale images are proposed. The first is a median filter modification based on impulsive noise detector. The second is obtained by combining the selective median filter with a digital inpainting model. The noise removal in color image is obtained by extending the proposed filters for each color channel of the image. The experiments indicated that the proposed methods are powerful in restoring images with high densities noise.

Image segmentation is an essential tool to enhance the ability of computer systems to efficiently perform elementary cognitive tasks such as detection, recognition and tracking. In this thesis we concentrate on the investigation of two fundamental topics in the context of image segmentation: spectral clustering and seeded image segmentation. We introduce two new algorithms for those topics that, in summary, rely on Laplacian-based operators, spectral graph theory, and minimization of energy functionals. The effectiveness of both segmentation algorithms is verified by visually evaluating the resulting partitions against state-of-the-art methods as well as through a variety of quantitative measures typically employed as benchmark by the image segmentation community. Our spectral-based segmentation algorithm combines image decomposition, similarity metrics, and spectral graph theory into a concise and powerful framework. An image decomposition is performed to split the input image into texture and cartoon components. Then, an affinity graph is generated and weights are assigned to the edges of the graph according to a gradient-based inner-product function. From the eigenstructure of the affinity graph, the image is partitioned through the spectral cut of the underlying graph. Moreover, the image partitioning can be improved by changing the graph weights by sketching interactively. Visual and numerical evaluation were conducted against representative spectral-based segmentation techniques using boundary and partition quality measures in the well-known BSDS dataset. Unlike most existing seed-based methods that rely on complex mathematical formulations that typically do not guarantee unique solution for the segmentation problem while still being prone to be trapped in local minima, our segmentation approach is mathematically simple to formulate, easy-to-implement, and it guarantees to produce a unique solution. Moreover, the formulation holds an anisotropic behavior, that is, pixels sharing similar attributes are preserved closer to each other while big discontinuities are naturally imposed on the boundary between image regions, thus ensuring better fitting on object boundaries. We show that the proposed approach significantly outperforms competing techniques both quantitatively as well as qualitatively, using the classical GrabCut dataset from Microsoft as a benchmark. While most of this research concentrates on the particular problem of segmenting an image, we also develop two new techniques to address the problem of image inpainting and photo colorization. Both methods couple the developed segmentation tools with other computer vision approaches in order to operate properly. / Segmentar uma image é visto nos dias de hoje como uma prerrogativa para melhorar a capacidade de sistemas de computador para realizar tarefas complexas de natureza cognitiva tais como detecção de objetos, reconhecimento de padrões e monitoramento de alvos. Esta pesquisa de doutorado visa estudar dois temas de fundamental importância no contexto de segmentação de imagens: clusterização espectral e segmentação interativa de imagens. Foram propostos dois novos algoritmos de segmentação dentro das linhas supracitadas, os quais se baseiam em operadores do Laplaciano, teoria espectral de grafos e na minimização de funcionais de energia. A eficácia de ambos os algoritmos pode ser constatada através de avaliações visuais das segmentações originadas, como também através de medidas quantitativas computadas com base nos resultados obtidos por técnicas do estado-da-arte em segmentação de imagens. Nosso primeiro algoritmo de segmentação, o qual ´e baseado na teoria espectral de grafos, combina técnicas de decomposição de imagens e medidas de similaridade em grafos em uma única e robusta ferramenta computacional. Primeiramente, um método de decomposição de imagens é aplicado para dividir a imagem alvo em duas componentes: textura e cartoon. Em seguida, um grafo de afinidade é gerado e pesos são atribuídos às suas arestas de acordo com uma função escalar proveniente de um operador de produto interno. Com base no grafo de afinidade, a imagem é então subdividida por meio do processo de corte espectral. Além disso, o resultado da segmentação pode ser refinado de forma interativa, mudando-se, desta forma, os pesos do grafo base. Experimentos visuais e numéricos foram conduzidos tomando-se por base métodos representativos do estado-da-arte e a clássica base de dados BSDS a fim de averiguar a eficiência da metodologia proposta. Ao contrário de grande parte dos métodos existentes de segmentação interativa, os quais são modelados por formulações matemáticas complexas que normalmente não garantem solução única para o problema de segmentação, nossa segunda metodologia aqui proposta é matematicamente simples de ser interpretada, fácil de implementar e ainda garante unicidade de solução. Além disso, o método proposto possui um comportamento anisotrópico, ou seja, pixels semelhantes são preservados mais próximos uns dos outros enquanto descontinuidades bruscas são impostas entre regiões da imagem onde as bordas são mais salientes. Como no caso anterior, foram realizadas diversas avaliações qualitativas e quantitativas envolvendo nossa técnica e métodos do estado-da-arte, tomando-se como referência a base de dados GrabCut da Microsoft. Enquanto a maior parte desta pesquisa de doutorado concentra-se no problema específico de segmentar imagens, como conteúdo complementar de pesquisa foram propostas duas novas técnicas para tratar o problema de retoque digital e colorização de imagens.

Abstract Three-dimensional scene reconstruction has been an important area of research for many decades. It has a myriad of applications ranging from entertainment to medicine. This thesis explores the 3D reconstruction pipeline and proposes novel methods to improve many of the steps necessary to achieve a high quality reconstruction. It proposes novel methods in the areas of depth sensor calibration, simultaneous localization and mapping, depth map inpainting, point cloud simplification, and free-viewpoint rendering. Geometric camera calibration is necessary in every 3D reconstruction pipeline. This thesis focuses on the calibration of depth sensors. It presents a review of sensors models and how they can be calibrated. It then examines the case of the well-known Kinect sensor and proposes a novel calibration method using only planar targets. Reconstructing a scene using only color cameras entails di_erent challenges than when using depth sensors. Moreover, online applications require real-time response and must update the model as new frames are received. The thesis looks at these challenges and presents a novel simultaneous localization and mapping system using only color cameras. It adaptively triangulates points based on the detected baseline while still utilizing non-triangulated features for pose estimation. The thesis addresses the extrapolating missing information in depth maps. It presents three novel methods for depth map inpainting. The first utilizes random sampling to fit planes in the missing regions. The second method utilizes a 2nd-order prior aligned with intensity edges. The third method learns natural filters to apply a Markov random field on a joint intensity and depth prior. This thesis also looks at the issue of reducing the quantity of 3D information to a manageable size. It looks at how to merge depth maps from multiple views without storing redundant information. It presents a method to discard this redundant information while still maintaining the naturally variable resolution. Finally, transparency estimation is examined in the context of free-viewpoint rendering. A procedure to estimate transparency maps for the foreground layers of a multi-view scene is presented. The results obtained reinforce the need for a high accuracy 3D reconstruction pipeline including all the previously presented steps. / Tiivistelmä Kolmiuloitteisen ympäristöä kuvaavan mallin rakentaminen on ollut tärkeä tutkimuksen kohde jo usean vuosikymmenen ajan. Sen sovelluskohteet ulottuvat aina lääketieteestä viihdeteollisuuteen. Väitöskirja tarkastelee 3D ympäristöä kuvaavan mallin tuottamisprosessia ja esittää uusia keinoja parantaa korkealaatuisen rekonstruktion tuottamiseen vaadittavia vaiheita. Työssä esitetään uusia menetelmiä etäisyyssensoreiden kalibrointiin, samanaikaisesti tapahtuvaan paikannukseen ja kartoitukseen, syvyyskartan korjaamiseen, etäisyyspistepilven yksinkertaistamiseen ja vapaan katselukulman kuvantamiseen. Väitöskirjan ensi osa keskittyy etäisyyssensoreiden kalibrointiin. Työ esittelee erilaisia sensorimalleja ja niiden kalibrointia. Yleisen tarkastelun lisäksi keskitytään hyvin tunnetun Kinect-sensorin käyttämiseen, ja ehdotetaan uutta kalibrointitapaa pelkkiä tasokohteita hyväksikäyttäen. Pelkkien värikameroiden käyttäminen näkymän rekonstruointiin tuottaa erilaisia haasteita verrattuna etäisyyssensoreiden käyttöön kuvan muodostamisessa. Lisäksi verkkosovellukset vaativat reaaliaikaista vastetta. Väitös tarkastelee kyseisiä haasteita ja esittää uudenlaisen yhtäaikaisen paikannuksen ja kartoituksen mallin tuottamista pelkkiä värikameroita käyttämällä. Esitetty tapa kolmiomittaa adaptiivisesti pisteitä taustan pohjalta samalla kun hyödynnetään eikolmiomitattuja piirteitä asentotietoihin. Työssä esitellään kolme uudenlaista tapaa syvyyskartan korjaamiseen. Ensimmäinen tapa käyttää satunnaispisteitä tasojen kohdentamiseen puuttuvilla alueilla. Toinen tapa käyttää 2nd-order prior kohdistusta ja intensiteettireunoja. Kolmas tapa oppii filttereitä joita se soveltaa Markov satunnaiskenttiin yhteisillä tiheys ja syvyys ennakoinneilla. Tämä väitös selvittää myös mahdollisuuksia 3D-information määrän pienentämiseen käsiteltävälle tasolle. Työssä selvitetään, kuinka syvyyskarttoja voidaan yhdistää ilman päällekkäisen informaation tallentamista. Työssä esitetään tapa jolla päällekkäisestä datasta voidaan luopua kuitenkin säilyttäen luonnollisesti muuttuva resoluutio. Viimeksi, tutkimuksessa on esitetty läpinäkyvyyskarttojen arviointiproseduuri etualan kerroksien monikatselukulmanäkymissä vapaan katselukulman renderöinnin näkökulmasta. Saadut tulokset vahvistavat tarkan 3D-näkymän rakentamisliukuhihnan tarvetta sisältäen kaikki edellä mainitut vaiheet.

This research presents a novel framework for automatic chroma keying and the optimizations for real-time and stereoscopic 3D processing. It first simulates the process of human perception on isolating foreground elements in a given scene by perceptual analysis, and then predicts foreground colours and alpha map based on the analysis results and the restored clean background plate rather than direct sampling. Besides, an object level depth map is generated through stereo matching on a carefully determined feature map. In addition, three prototypes on different platforms have been implemented according to their hardware capability based on the proposed framework. To achieve real-time performance, the entire procedures are optimized for parallel processing and data paths on the GPU, as well as heterogeneous computing between GPU and CPU. The qualitative comparisons between results generated by the proposed algorithm and other existing algorithms show that the proposed one is able to generate more acceptable alpha maps and foreground colours especially in those regions that contain translucencies and details. And the quantitative evaluations also validate our advantages in both quality and speed.

This thesis deals with audio inpainting problem. Firstly, basic concepts are summarized. Then, sparse representation of signals is introduced along with several algorithms. In the main part dedicated to the audio inpainting, the problem is defined and actual methods are presented and compared. The newest approach using the harmonic strucure of sound signals is then introduced, followed by several experiments and evaluation. Lastly, an algorithm ensuring the maximal computational efficiency is derived.

Les problèmes inverses consistent à retrouver une donnée qui a été transformée ou perturbée. Ils nécessitent une régularisation puisque mal posés. En traitement d'images, la variation totale en tant qu'outil de régularisation a l'avantage de préserver les discontinuités tout en créant des zones lisses, résultats établis dans cette thèse dans un cadre continu et pour des énergies générales. En outre, nous proposons et étudions une variante de la variation totale. Nous établissons une formulation duale qui nous permet de démontrer que cette variante coïncide avec la variation totale sur des ensembles de périmètre fini. Ces dernières années les méthodes non-locales exploitant les auto-similarités dans les images ont connu un succès particulier. Nous adaptons cette approche au problème de complétion de spectre pour des problèmes inverses généraux. La dernière partie est consacrée aux aspects algorithmiques inhérents à l'optimisation des énergies convexes considérées. Nous étudions la convergence et la complexité d'une famille récente d'algorithmes dits Primal-Dual.

Cílem této práce je vytvořit neuronovou síť která bude schopna rekonstruovat obličeje z fotografií na kterých je část obličeje překrytá maskou. Jsou prezentovány koncepty využívané při vývoji konvolučních neuronových sítí a generativních kompetitivních sítí. Dále jsou popsány koncepty používané v neuronových sítích specificky pro rekonstrukci fotografií obličejů. Je představen model generativní kompetitivní sítě využívající kombinaci hrazených konvolučních vrstev a víceškálových bloků schopný realisticky doplnit oblasti obličeje zakryté maskou.

Cette thèse concerne le problème de désocclusion d'images, au moyen des équations aux dérivées partielles. Dans la première partie de la thèse, la désocclusion est modélisée par un problème de Cauchy qui consiste à déterminer une solution d'une équation aux dérivées partielles avec des données aux bords accessibles seulement sur une partie du bord de la partie à recouvrir. Ensuite, on a utilisé des algorithmes de minimisation issus de la théorie des jeux, pour résoudre ce problème de Cauchy. La deuxième partie de la thèse est consacrée au choix des paramètres de régularisation pour des EDP d'ordre deux et d'ordre quatre. L'approche développée consiste à construire une famille de problèmes d'optimisation bien posés où les paramètres sont choisis comme étant une fonction variable en espace. Ceci permet de prendre en compte les différents détails, à différents échelles dans l'image. L'apport de la méthode est de résoudre de façon satisfaisante et objective, le choix du paramètre de régularisation en se basant sur des indicateurs d'erreur et donc le caractère à posteriori de la méthode (i.e. indépendant de la solution exacte, en générale inconnue). En outre, elle fait appel à des techniques classiques d'adaptation de maillage, qui rendent peu coûteuses les calculs numériques. En plus, un des aspects attractif de cette méthode, en traitement d'images est la récupération et la détection de contours et de structures fines. / Image inpainting refers to the process of restoring a damaged image with missing information. Different mathematical approaches were suggested to deal with this problem. In particular, partial differential diffusion equations are extensively used. The underlying idea of PDE-based approaches is to fill-in damaged regions with available information from their surroundings. The first purpose of this Thesis is to treat the case where this information is not available in a part of the boundary of the damaged region. We formulate the inpainting problem as a nonlinear boundary inverse problem for incomplete images. Then, we give a Nash-game formulation of this Cauchy problem and we present different numerical which show the efficiency of the proposed approach as an inpainting method.Typically, inpainting is an ill-posed inverse problem for it most of PDEs approaches are obtained from minimization of regularized energies, in the context of Tikhonov regularization. The second part of the thesis is devoted to the choice of regularization parameters in second-and fourth-order energy-based models with the aim of obtaining as far as possible fine features of the initial image, e.g., (corners, edges, … ) in the inpainted region. We introduce a family of regularized functionals with regularization parameters to be selected locally, adaptively and in a posteriori way allowing to change locally the initial model. We also draw connections between the proposed method and the Mumford-Shah functional. An important feature of the proposed method is that the investigated PDEs are easy to discretize and the overall adaptive approach is easy to implement numerically.

Cette thèse présente de nouvelles méthodes de synthèse de texture basées sur l'exemple pour les problèmes de prédiction d'images (c'est à dire, codage prédictif) et d'inpainting d'images. Les principales contributions de cette étude peuvent aussi être vues comme des extensions du template matching. Cependant, le problème de synthèse de texture tel que nous le définissons ici se situe plutôt dans un contexte d'optimisation formalisée sous différentes contraintes. Le problème de prédiction d'images est d'abord situé dans un contexte de représentations parcimonieuses par l'approximation du template sous contraintes de parcimonie. La méthode de prédiction proposée avec les dictionnaires adaptés localement montrent de meilleures performances par rapport aux dictionnaires classiques (tels que la transformée en cosinus discrète (TCD)), et à la méthode du template matching. Le problème de prédiction d'images est ensuite placé dans un cadre d'apprentissage de dictionnaires en adaptant les méthodes traditionnelles d'apprentissage pour la prédiction de l'image. Les observations expérimentales montrent une meilleure performance comparativement à des méthodes de prédiction parcimonieuse et des prédictions intra de type H.264/AVC. Enfin un cadre neighbor embedding est proposé pour la prédiction de l'image en utilisant deux méthodes de réduction de dimensionnalité: la factorisation de matrice non négative (FMN) et le locally linear embedding (LLE). Ce cadre est ensuite étendu au problème d'inpainting d'images. Les évaluations expérimentales démontrent l'efficacité des idées sous-jacentes pour la compression via la prédiction d'images et l'inpainting d'images.

Made available in DSpace on 2014-06-11T19:26:56Z (GMT). No. of bitstreams: 0 Previous issue date: 2005-02-25Bitstream added on 2014-06-13T19:47:24Z : No. of bitstreams: 1 santos_ca_me_sjrp.pdf: 757765 bytes, checksum: bd1f77ee4f0f4cdebfc0a29af4d9bc39 (MD5) / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / O objetivo deste trabalho þe aplicar Mþetodos Numþericos de ordem de precisão mais alta ao problema de Retoque Digital, visando melhorar a qualidade da aproximação quando comparada com o Método de Euler, que þe geralmente utilizado para esse tipo de problema. Para testar a eficiência de tais métodos, utilizamos três modelos de Retoque Digital: o modelo proposto por Bertalmþýo, Sapiro, Ballester e Caselles (BSBC), o modelo de Rudin, Osher e Fatemi conhecido como Variacional Total (TV) e o modelo de Chan e Shen, chamado de Difusão Guiada pela Curvatura (CDD). / The purpose of this work is to apply Numerical Methods of higher order to the problem of Digital Inpainting, aiming to improve the quality of the approach when compared with the Euler s Method which is generally used for this kind of problem. To test the e ciency of these methods we use three models of Digital Inpainting: the model considered by Bertalmþýo, Sapiro, Ballester and Caselles (BSBC), the model of Rudin, Osher and Fatemi known as Total Variation (TV) and the model of Chan and Shen, named Curvature Driven Di usion (CDD)